Understanding Algorithms For Big Data Compsci 229r Lecture 20
If you are looking for information about Algorithms For Big Data Compsci 229r Lecture 20, you have come to the right place. Krahmer-Ward proof, Iterative Hard Thresholding.
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 20
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- Matrix completion.
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 20
ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
We hope this detailed breakdown of Algorithms For Big Data Compsci 229r Lecture 20 was helpful.
